Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery

Abstract

Deep learning (DL) methods combined with computational simulations have shown promise for novel material discovery and establishing structure–response relationships within extensive design spaces. However, obtaining sufficient simulation data for precise predictions on nonlinear responses remains challenging, limiting DL models' predictive capabilities for unexplored composite configurations. To address this, we introduce the hierarchical generative network (HGNet), comprising three customized convolutional neural networks (NNs). HGNet predicts stress fields and crack patterns during mechanical failure events, encompassing linear response regimes, ultimate strength, and complete fracture. By jointly training these networks within a unified DL model, predictive capacity significantly improves. HGNet is applied to a two‐phase composite optimization problem with vast design configurations, demonstrating accurate predictions of complex stress distributions and fracture patterns in unseen configurations. Moreover, it explores uncharted designs with superior stiffness and strength across vast design spaces using genetic algorithms. The methodology provides insight into the model's reasoning through visualizing the inference process. Overall, this approach represents a potent and versatile tool for accelerating material discovery, facilitating a more efficient and cost‐effective approach to finding novel materials, even with limited training data.